--- title: Kubeflow at KubeCon Europe 2019 in Barcelona description: Kubeflow, is a Kubernetes native application for AI and Machine Learning, for those attending KubeCon Europe 2019 in Barcelona, you can learn more about it. url: https://canonical-com-2950.demos.haus/blog/kubeflow-at-kubecon-europe-2019-in-barcelona?format=md --- 1. [Blog](https://canonical-com-2950.demos.haus/blog) 2. Article --- [Carmine Rimi](https://canonical-com-2950.demos.haus/blog/author/carmine-rimi "More about Carmine Rimi") 20 May 2019 # Kubeflow at KubeCon Europe 2019 in Barcelona [AI](https://canonical-com-2950.demos.haus/blog/tag/ai) [AI/ML](https://canonical-com-2950.demos.haus/blog/tag/ai-ml) [artificial intelligence](https://canonical-com-2950.demos.haus/blog/tag/artificial-intelligence) [KubeCon](https://canonical-com-2950.demos.haus/blog/tag/kubecon) [Kubeflow](https://canonical-com-2950.demos.haus/blog/tag/kubeflow) [kubernetes](https://canonical-com-2950.demos.haus/blog/tag/kubernetes) [machine learning](https://canonical-com-2950.demos.haus/blog/tag/machine-learning) [ML](https://canonical-com-2950.demos.haus/blog/tag/ml) [MLOps](https://canonical-com-2950.demos.haus/blog/tag/mlops) --- Share the article [Kubeflow,](https://kubeflow.org) the Kubernetes native application for AI and Machine Learning, continues to accelerate feature additions and community growth. The community has released two new versions since the last Kubecon – 0.4 in January and 0.5 in April – and is currently working on the 0.6 release, to be released in July. The key features in each release are briefly discussed below. # Kubeflow at Kubecon For those attending KubeCon + CloudNativeCon Europe 2019 in Barcelona, you can learn more about Kubeflow and how to apply it to your business in the following sessions: #### TUESDAY, May 21 | | | | --- | --- | | 14:00 | [Kubernetes the New Research Platform](https://kccnceu19.sched.com/event/MPY6/kubernetes-the-new-research-platform-lindsey-tulloch-brock-university-bob-killen-university-of-michigan) – Lindsey Tulloch, Brock University & Bob Killen, University of Michigan | | 14:00 | [Tutorial: Introduction to Kubeflow Pipelines](https://kccnceu19.sched.com/event/MPgr/tutorial-introduction-to-kubeflow-pipelines-michelle-casbon-dan-sanche-dan-anghel-michal-zylinski-google) – Michelle Casbon, Dan Sanche, Dan Anghel, & Michal Zylinski, Google | | 15:55 | [KubeFlow BoF (Birds of a Feather)](https://kccnceu19.sched.com/event/PiUF/kubeflow-bof-birds-of-a-feather-david-aronchick-microsoft-yaron-haviv-iguazio) – David Aronchick, Microsoft & Yaron Haviv, Iguazio | #### WEDNESDAY, May 22 | | | | --- | --- | | 11:55 | [Towards Kubeflow 1.0, Bringing a Cloud Native Platform For ML to Kubernetes](https://kccnceu19.sched.com/event/MPax/towards-kubeflow-10-bringing-a-cloud-native-platform-for-ml-to-kubernetes-david-aronchick-microsoft-jeremy-lewi-google) – David Aronchick, Microsoft & Jeremy Lewi, Google | | 14:00 | [Building Cross-Cloud ML Pipelines with Kubeflow with Spark & Tensorflow](https://kccnceu19.sched.com/event/MPaZ/building-cross-cloud-ml-pipelines-with-kubeflow-with-spark-tensorflow-holden-karau-google-trevor-grant-ibm) – Holden Karau, Google & Trevor Grant, IBM | | 14:50 | [Managing Machine Learning in Production with Kubeflow and DevOps](https://kccnceu19.sched.com/event/MPar/managing-machine-learning-in-production-with-kubeflow-and-devops-david-aronchick-microsoft) – David Aronchick, Microsoft | #### THURSDAY, May 23 | | | | --- | --- | | 11:55 | [A Tale of Two Worlds: Canary-Testing for Both ML Models and Microservices](https://kccnceu19.sched.com/event/MPau/a-tale-of-two-worlds-canary-testing-for-both-ml-models-and-microservices-jorg-schad-arangodb-vincent-lesierse-vampio) – Jörg Schad, ArangoDB & Vincent Lesierse, Vamp.io | | 14:00 | [Moving People and Products with Machine Learning on Kubeflow](https://kccnceu19.sched.com/event/MPac/moving-people-and-products-with-machine-learning-on-kubeflow-jeremy-lewi-google-willem-pienaar-go-jek) – Jeremy Lewi, Google & Willem Pienaar, GO-JEK | | 14:50 | [Economics and Best Practices of Running AI/ML Workloads on Kubernetes](https://kccnceu19.sched.com/event/MPaf/economics-and-best-practices-of-running-aiml-workloads-on-kubernetes-maulin-patel-google-yaron-haviv-iguazio) – Maulin Patel, Google & Yaron Haviv, Iguazio | Come by the Canonical booth to learn how to get started with Kubeflow quickly and easily – on Ubuntu with [Microk8s](https://microk8s.io), and on Windows or macOS with [Multipass](https://multipass.run) and [Microk8s](https://microk8s.io). # What’s in Kubeflow 0.5? * This is a summary of some of the key features: * UI Improvements – new Central Dashboard and a new sidebar navigation * JupyterHub Improvements – launch multiple notebooks, attach volume * Fairing Python Library – build, train, and deploy models from notebooks or IDE * Katib (hyperparameter) Improvements – more generic, updated CRD, better status * KFCTL binary (configure and platform deploy). () * Pipelines Persistence ([upgrade or reinstall](https://www.kubeflow.org/docs/pipelines/upgrade/)) * 150+ closed issues and 250+ merged PRs You can learn more about the 0.5 release from the [Kubeflow blog on 0.5](https://medium.com/kubeflow/kubeflow-v0-5-simplifies-model-development-with-enhanced-ui-and-fairing-library-78e19cdc9f50). # What’s in Kubeflow 0.4? * An updated [JupyterHub UI](https://github.com/kubeflow/kubeflow/tree/master/components/jupyterhub) that makes it easy to spawn notebooks with Persistent Volume Claims (PVCs). * An alpha release of [fairing](https://github.com/kubeflow/fairing), a python library that simplifies the build and train process for data scientists – they can start training jobs directly from a notebook or IDE. * An initial release of a Custom Resource Definition (CRD) for managing Jupyter notebooks. You can use kubectl to create notebook containers. * [Kubeflow Pipelines](https://github.com/kubeflow/pipelines) for orchestrating ML workflows, which speeds the process of productizing models by reusing pipelines with different datasets or updated data. * [Katib](https://github.com/kubeflow/katib) support for TFJob, which makes it easier to tune models and compare performance with different hyper-parameters. * Beta versions of the [TFJob](https://github.com/kubeflow/tf-operator) and [PyTorch](https://github.com/kubeflow/pytorch-operator) operators, which enable data scientists to program their training jobs against a more stable API and to more easily switch between training frameworks. You can learn more about the 0.4 release from the [Kubeflow blog on 0.4](https://medium.com/kubeflow/kubeflow-0-4-release-enhancements-for-machine-learning-productivity-d77c54df07a9). # Learn more about Kubeflow There is a wealth of information at [kubeflow.org](https://kubeflow.org), this includes docs, blogs, and examples. In addition, you can go to [ubuntu.com/ai/install](https://ubuntu.com/ai/install) to get started quickly. ## Sign up for our newsletter Get the latest Canonical news and updates in your inbox. Work email: \*I agree to receive information about Canonical's products and services. By submitting this form, I confirm that I have read and agree to [Canonical's Privacy Policy](https://canonical-com-2950.demos.haus/legal/dataprivacy). 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